Systems and methods for generating a message topic training dataset from user interactions in message clients

ABSTRACT

Systems and methods for classifying messages are provided. Each message in a plurality of messages is classified, thereby independently identifying a message category in a set of message categories for each respective message in the plurality. The plurality of messages is delivered to a plurality of recipients with a designation of the message category of each respective message in the first plurality of messages. A plurality of recipient initiated message interaction events for messages in the first plurality of messages over a predetermined period of time is collected from the plurality of recipients. A message categorization dataset is then constructed from (i) the first plurality of messages, (ii) the designation of the message category of each respective message in the subset of the first plurality of messages, and (iii) the plurality of recipient initiated message interaction events. This message categorization dataset is used to train or evaluate a message classifier.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/555,312, filed Nov. 26, 2014, the entire disclosure of whichis hereby incorporated herein by reference.

TECHNICAL FIELD

This specification describes technologies relating to an email system ingeneral, and specifically to systems and methods for generating amessage topic training dataset from user interactions in messageclients. This message topic training dataset can then be used to trainor evaluate the performance of a test message classifier.

BACKGROUND

Electronic messaging, such as through email, is a powerful communicationtool for the dissemination of information. However, the ease of sendingmessages can result in a recipient receiving large numbers of messagesin a single day. This is because, in addition to message sent by actualpeople, a recipient may receive messages generated by machines fromthird party services such as airlines, invitation generating companies,courier services, and social media sites. These messages may includeconfirmations, notifications, promotions, social media updates, andmessages from collaboration system.

To address the large number of messages received by recipients, modernmessaging systems attempt to classify messages for recipients using aset of message categories. That is, as part of the delivery process ofmessages to recipients, the messaging system reviews the messages anddetermines which category, from a set of message categories, to assignthe message. However, the appropriate classification of messages intosuch categories remains a challenging problem. One reason for this isthat there is a limited amount of information in messages (by which tocategorize such messages). Another reason message categorization remainsa challenge is due to the often idiosyncratic, nonorthogonal, and highlysubjective characteristics of the message categories themselves. Inshort, the messages to be categorized often have very little informationthat can be used to reliably categorize them, and the message categorieswithin any given set of message categories often have features thatoverlap with other message categories in the set.

The above identified technical problems are reduced or eliminated by thesystems and methods disclosed herein.

SUMMARY

Technical solutions (e.g., computing systems, methods, andnon-transitory computer readable storage mediums) for classifyingmessages are provided in the present application. Since there isn't a“ground truth” for message categories, the disclosed systems and methodsuse a proxy for the true category of messages in order to applysupervised learning. In some embodiments such proxies are based on theclassifier output from a traditional classifier in combination with userinteraction data.

For instance, some embodiments make use of the observation of an absenceof corrections in a given set of messages. To this end, a list ofsenders is maintained that generate messages that are consistentlycategorized by a classifier into the same set of categories, generate alot of messages, and for which there are very few recipient correctionsto such categorization. The classification of these messages can be moretrusted than the classification of other messages. As such, messagesfrom this list of senders can be used as the basis of a messagecategorization dataset in which the message category of each message istrusted. This message categorization dataset can be used to train amessage classifier or evaluate the performance of such a messageclassifier.

As another example, some embodiments make use of the observation of apresence of corrections to the message category by message recipient.That is, those messages received by message recipients corrected intosome category X from category Y, are presumed to have higher confidencethat such message are really category X than the confidence that suchmessages are category Y as predicted by a message classifier. This isbecause user judgment of message categories is valued in such instancesfor the judgment of an automated classifier. Messages that have beenrecategorized in this way by message recipients can be used as the basisof a message categorization dataset in which the message category ofeach message is trusted. This message categorization dataset can be usedto train or evaluate the performance of a message classifier.

As another example, some embodiments make use of the presence ofnegative recipient interactions with received messages to gain trustthat the message categories assigned by an automated classifier to suchreceived messages is correct. The presumption in such examples is that auser would not interact with such messages if their classifications werenot correct. Examples of such positive interaction signals include, butare not limited to, the time spent reading such messages, theapplication of importance markers to such messages, as well as actionssuch as replying to or forwarding of such messages. From these actions,implicit agreement with the classifier categorization of such messagescan be inferred. Messages that have been verified in this implicit wayby message recipients can be used as the basis for a messagecategorization dataset in which the message category of each message istrusted. This message categorization dataset can be used to train ormeasure the performance of a message classifier.

In some embodiments, messages that have been identified by any one ofthe above-identified ways as trusted are combined into a single messagecategorization dataset. In some embodiments, rather than simply applyinga binary filter to each message, such that the message is eitherexcluded from or included in the message categorization dataset, theabove-identified information is used to assign a weight to each messagein the message categorization dataset. Those messages whose messagecategories are more trusted are upweighted relative to those messagesthat are less trusted. In some embodiments, messages that are upweightedhave greater influence during classifier training than those messagesthat are less trusted.

The present disclosure is not limited to identifying messages whosemessage categories are trusted. In fact, methods for identifyingmessages whose message categories are not trusted are also provided. Asan example, some embodiments make use of the presence of negativerecipient interactions with received messages to determine that themessage categories assigned by an automated classifier to such receivedmessages are not correct. The presumption is in such embodiments that auser would not incur these negative interactions with such messages iftheir classification were correct. Examples of such negative interactionsignals include, but are not limited to, ignoring such messages, or thedeletion of such messages. From these actions, implicit disagreementwith the classifier categorization of such messages can be inferred.Messages that have been discounted in this implicit way by messagerecipients can be used as the basis of excluding them from (ordownweighting them in) a message categorization dataset in which themessage category of each message is trusted, thereby enriching themessage categorization dataset for messages whose messagecategorizations are trusted. This message categorization dataset can beused to train or measure the performance of a message classifier.

Various embodiments of systems, methods and devices within the scope ofthe appended claims each have several aspects, no single one of which issolely responsible for the desirable attributes described herein.Without limiting the scope of the appended claims, some prominentfeatures are described herein. After considering this discussion, andparticularly after reading the section entitled “Detailed Description”one will understand how the features of various embodiments are used.

In some implementations, a method is provided for classifying messages.The method comprises, at a computer system having one or moreprocessors, and memory storing one or more programs for execution by theone or more processors, classifying each message in a first plurality ofmessages. In this way, a message category in a set of message categoriesis independently identified for each respective message in the firstplurality of messages. The first plurality of messages is delivered to aplurality of recipients with a designation of the message category ofeach respective message in the first plurality of messages. Typicallythe recipient is made aware of the message categories of these messages(e.g., by including messages or each respective message in a differentmessage tab as illustrated in FIG. 5). A plurality of recipientinitiated message interaction events is collected for messages in thefirst plurality of messages (e.g., over a predetermined period of time,on a recurring basis, etc.) from the plurality of recipients. A messagecategorization dataset is then constructed from (i) the first pluralityof messages, (ii) the designation of the message category of eachrespective message in the plurality of messages, and (iii) and theplurality of recipient initiated message interaction events. Thismessage categorization dataset is then used to train or measure theperformance of a test message classifier. Because the messagecategorization dataset is enriched for trusted message categorizationinformation, the accuracy and/or some other performance metric (e.g., acost function) of the test message classifier is improved. In someembodiments, the initial classifying at the outset of the method isperformed by a baseline message classifier that was constructed at atime prior to the classifying.

In some embodiments, the set of message categories comprises promotions,social, updates, forums, travel, finance and receipts or anysubcombination thereof. In some embodiments, the set of messagecategories comprises priority, promotions, social, updates, and forums.In some embodiments, the set of message categories is defined by arecipient. Any categorical set of message categories may be used in thesystems and methods of the present disclosure.

In some embodiments, each recipient in the plurality of recipients isassociated with a client in a plurality of clients and the deliveringthe first plurality of messages comprises delivering each respectivemessage in the first plurality of messages to the client in theplurality of clients that is associated with the recipient of therespective message. That is to say, in some embodiments each recipientreceives messages via a client device running a messaging system.

In some embodiments, the classifying is performed by a baseline messageclassifier that was constructed at a time prior to the classifying andthe plurality of recipient initiated message interaction events aremessage category correction events in which a recipient has changed thecategory of a message from the message category assigned by the baselinemessage classifier to a different message category in the set of messagecategories. In some such embodiments, the constructing comprisesidentifying a plurality of senders (i) whose messages in the firstplurality of messages are consistently categorized by the baselinemessage classifier into the same respective message categories in theset of message categories and (ii) whose messages are associated withless than a predetermined amount of message interaction events. In otherwords, the messages from such senders are rarely recategorized byrecipients and the automated baseline classifier typically classifiesthe messages from such senders into the same limited few (or single)message classification. In some embodiments, it is the messages fromthis plurality of senders that is used as the basis for the messagecategorization dataset. In other embodiments, the messages from thisplurality of senders is upweighted relative to other messages in themessage categorization dataset, such that the upweighted messages havegreater influence on classifier training than messages that have notbeen upweighted. In some embodiments, messages in the first plurality ofmessages are consistently categorized by the baseline message classifierinto the same respective message categories in the set of messagecategories when at least a predetermined threshold percentage of themessages (e.g., at least seventy percent, at least eighty percent, etc.)from the plurality of senders are categorized into two or less messagecategories in the set of message categories by the baseline messageclassifier. In some such embodiments, each sender in the plurality ofsenders has more than a threshold number of messages (e.g, more than 10messages, more than 100 messages, etc.) in the first plurality ofmessages. In some such embodiments, the message categorization datasetcomprises a subset of the first plurality of messages.

In some embodiments, the message categorization dataset comprises asubset of the first plurality of messages and the classifying isperformed by a baseline message classifier that was constructed at atime prior to the classifying. Further, in some such embodiments, theplurality of recipient initiated message interaction events are eventsin which the recipient changes the category of a message from themessage category assigned by the baseline message classifier to adifferent message category in the set of message categories. In somesuch embodiments, the constructing comprises selecting, for the subsetof the first plurality of messages, those messages in the firstplurality of messages that have undergone a recipient initiated messageinteraction event.

In some embodiments, the constructing comprises selecting, for a subsetof the first plurality of messages, those messages in the firstplurality of messages associated with a recipient initiated messageinteraction event. In some embodiments, the recipient initiated messageinteraction event comprises a recipient of a respective message in thefirst plurality of messages opening the respective message for at leasta predetermined amount of time. In some embodiments, the recipientinitiated message interaction event comprises a recipient of arespective message in the first plurality of messages assigning therespective message a priority designation. In some such embodiments, therecipient initiated message interaction event comprises a recipient of arespective message in the first plurality of messages replying to therespective message. In some embodiments, the recipient initiated messageinteraction event comprises a recipient of a respective message in thefirst plurality of messages forwarding the respective message.

In some embodiments, the message categorization dataset comprises asubset of the first plurality of messages, and the constructingcomprises excluding from the subset of the first plurality of messagesthose messages in the first plurality of messages associated with arecipient initiated message interaction event. In some such embodiments,the classifying is performed by a baseline message classifier that wasconstructed at a time prior to the classifying, and the recipientinitiated message interaction event comprises a recipient changing amessage category of a respective message in the first plurality ofmessages that was assigned to the respective message by the baselinemessage classifier. In some such embodiments, the recipient initiatedmessage interaction event comprises a recipient deleting a respectivemessage in the first plurality of messages.

In some embodiments, the method further comprises classifying eachmessage in a second plurality of messages using the test messageclassifier (whose performance has been measured by the messagecategorization dataset 315 or that was trained by this dataset 315),thereby independently identifying a message category in the set ofmessage categories for each respective message in the second pluralityof messages. Further, the second plurality of messages is delivered tothe plurality of recipients with a designation of the message categoryof each respective message in the second plurality of messages, asrespectively determined by the test message classifier.

In some embodiments, the message categorization dataset 315 is used totrain a test message classifier or measure the performance of the testmessage classifier. In some embodiments, the test message classifier andthe baseline message classifier are the same message classifier.

In some embodiments, each message in the first plurality of messagescomprises an Email, short message service (SMS) text message, amultimedia messaging service (MMS) message, a file, a document, a video,an image, or an electronic conversation.

In some embodiments, the constructing a message categorization datasetcomprises independently assigning a weight to a respective message inthe plurality of messages based upon one or more recipient initiatedmessage interaction events that are associated with the respectivemessage. In some embodiments, the weight is a categorical weight, andthe assigning assigns a first value to the weight when the one or morerecipient initiated message interaction events are positiveinteractions, and the assigning assigns a second value to the weightwhen the one or more recipient initiated message interaction events arenegative interactions. In some embodiments, the weight is a on acontinuous scale, and the assigning assigns a value to the weight as afunction of the one or more recipient initiated message interactionevents associated with the respective message.

Another aspect of the present disclosure provides a computing system,comprising one or more processors and memory storing one or moreprograms to be executed by the one or more processors. The one or moreprograms comprise instructions for performing any of the methodsdisclosed herein.

Another aspect of the present disclosure provides a non-transitorycomputer readable storage medium storing one or more programs configuredfor execution by a computer. The one or more programs compriseinstructions for performing any of the methods disclosed herein.

Thus, these methods, systems, and -transitory computer readable storagemedium provide new, less cumbersome, more efficient ways to classifymessages with better performance.

BRIEF DESCRIPTION OF THE DRAWINGS

The implementations disclosed herein are illustrated by way of example,and not by way of limitation, in the figures of the accompanyingdrawings. Like reference numerals refer to corresponding partsthroughout the drawings.

FIG. 1 is an example block diagram illustrating a computing system, inaccordance with some implementations.

FIG. 2 is an example block diagram illustrating a computing device, inaccordance with some implementations.

FIG. 3A is an example block diagram illustrating a computing system, inaccordance with some implementations.

FIG. 3B illustrates a recipient initiated message interaction datastore, in accordance with some implementations.

FIG. 3C illustrates a message categorization dataset, in accordance withsome implementations.

FIG. 4 is an example flow chart illustrating a method for categorizingmessages, in accordance with some implementations.

FIG. 5 is a user interface in a device based message application thatenables message recipients to interact with received messages inaccordance with some embodiments.

FIGS. 6A-6D provide a flow chart of methods of classifying messages inaccordance with some implementations.

DETAILED DESCRIPTION

The implementations described herein provide various technical solutionsto improving the categorization of electronic messages generally, and inparticular to improved methods for identified datasets that can be usedto reliably train classifiers or to measure their accuracy. Details ofimplementations are now described in relation to the Figures.

FIG. 1 is a block diagram illustrating a computing system 100, inaccordance with some implementations. In some implementations, thecomputing system 100 includes one or more devices 102 (e.g., device102A, 102B, 102C, 102D . . . , and 102N), a communication network 104,and a categorization system 106. In some implementations, a device 102is a phone (mobile or landline, smart phone or otherwise), a tablet, acomputer (mobile or otherwise), a fax machine, or an audio/videorecorder.

In some implementations, a device 102 obtains an electronic message from(e.g., drafted or generated by) a user of the device 102, and transmitsthe electronic message to the categorization system 106 for displayingwith other electronic messages. For example, after determining that userJack sends an electronic message to recipient Mary, the device 102transmits the electronic message to the categorization system 106 fordelivery to the device 102 associated with recipient Mary.

In some implementations, an electronic message is a file transfer 111-a(e.g., a photo, document, or video download/upload), an email 111-b, aninstant message 111-c, a fax message 111-d, a social network update111-e, or a voice message 111-f. In some implementations, an electronicmessage is a calendar entry, an email, a short message service (SMS)text message, a multimedia messaging service (MMS) message, a file, adocument, a video, an image, or an electronic conversation.

In some implementations, a device 102 includes a messaging application150. In some implementations, the messaging application 150 processesincoming and outgoing electronic messages into and from the device 102,such as an outgoing electronic message sent by a user of the device 102to another user, and a conversation message by another user to a user ofthe device 102. In some embodiments the messaging application 150 is anemail application.

In some implementations, the communication network 104 interconnects oneor more devices 102 with each other, and with the categorization system106. In some implementations, the communication network 104 optionallyincludes the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), other types of networks, or acombination of such networks.

With reference to FIG. 1, in some implementations, the classificationsystem 106 includes a number of components related to the development ofa message categorization dataset, including a message queue 112 thatincludes a plurality of messages 114-1 to 114-N, a message classifiermodule 170, and a classified message store 172. In some embodiments, themessages in the message queue 112 originated from users associated withdevices 102 and were communicated to categorization system 106 acrosscommunication network 104. In some implementations, the message queue112 includes different types of electronic messages, such as a filetransfer 111-a (e.g., a photo, document, or video upload), an email111-b, an instant message 111-c, a fax message 111-d, a social networkupdate 111-e, a voice message 111-f, contact information, an indicationof a document, a calendar entry, an email label, a recent search query,a suggested search query, or a web search result. In some embodiments,the messages in the message queue 112 constitute a training set ofmessages, such as the Enron corpus. See Klint and Yang, 2004, “The Enroncorpus: A new dataset for email classification research,” European Conf.Machine Learning, 217-226, which is hereby incorporated by reference. Insome implementations, the categorization system 106 invokes the messageclassifier module 170 to classify each message in the first plurality ofmessages 114-1 to 114-N thereby independently identifying an initialmessage category in a set of message categories for each respectivemessage in the plurality of messages.

An example of a set of message categories is {promotions, social,updates, forums, travel, finance and receipts}. Each message category inthe set of message categories requires that a message have certaincharacteristics. A message containing a reservation may be classified asan “update” message. A message containing information about an event maybe classified as a “promotion” message. If the message asks a recipientto rate something, the message may be a “social” message. In someembodiments, there is any number of additional messages categories.

By way of nonlimiting example, in some embodiments, messages that arelikely to be categorized as “promotions” are newsletters, offers andother bulk messages. In some embodiments, messages that are likely to becategorized as “social” are messages originating from social networkingwebsite. In some embodiments, messages that are likely to be categorizedas “updates” are confirmations, bills, and receipt messages. In someembodiments, messages that are likely to be categorized as “forum”messages are messages from online groups, discussion boards, and mailinglists. In some embodiments, messages that are likely to be categorizedas “primary” are messages that do not fall into any of the othercategories.

Once classified, messages in the first plurality of messages are storedin classified message store 172. In some embodiments, classified messagestore 172 includes only a reference to where such messages are stored(e.g., a reference to message queue or some other location where themessage is stored) and the classification of the message. Messages inmessage store 172 are distributed to the devices 102 associated with therecipients of these messages by message delivery module 174.

Categorization system 106 further includes a recipient initiated messageinteraction monitoring module 176 to monitor recipient initiated messageinteractions that occur on devices 102. Examples of message interactionsinclude, but are not limited to, changing the message category ofmessages that have been assigned by message classifier module 170, theopening of such messages, the review of such messages, as well as theforwarding or reply to such messages. Such recipient initiated messageinteraction is stored by categorization system 106 in recipientinitiated message interaction data store 182.

Message categorization dataset construction module 178 uses the eventsin data store 178 to form a message categorization data set 315. Themodule leverages the interaction events to adjust the level of trust ininitial message categorizations assigned to messages by messageclassifier module 170. Quantitatively, this level of trust takes eithera categorical form (e.g., present or absent) or a continuous form (e.g.,a continuous variable that value of which quantifies a level of trust).In the categorical form, messages in the plurality of messages whosecategories are not sufficiently trusted are excluded from the messagecategorization data set 315 whereas those messages that are sufficientlytrusted are included. In the continuous form, messages in the pluralityof messages whose categories are not trusted remain in the messagecategorization data set 315 but receive less weight than those messagesthat have greater trust are included.

FIG. 2 is a block diagram illustrating a computing device 102, inaccordance with some implementations. The device 102 in someimplementations includes one or more processing units CPU(s) 202 (alsoreferred to as processors), one or more network interfaces 204, a userinterface 205, a memory 206, and one or more communication buses 208 forinterconnecting these components. The communication buses 208 optionallyinclude circuitry (sometimes called a chipset) that interconnects andcontrols communications between system components. The memory 206typically includes high-speed random access memory, such as DRAM, SRAM,DDR RAM, ROM, EEPROM, flash memory, CD-ROM, digital versatile disks(DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, other randomaccess solid state memory devices, or any other medium which can be usedto store desired information; and optionally includes non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. The memory 206 optionally includes one or more storagedevices remotely located from the CPU(s) 202. The memory 206, oralternatively the non-volatile memory device(s) within the memory 206,comprises a non-transitory computer readable storage medium. In someimplementations, the memory 206 or alternatively the non-transitorycomputer readable storage medium stores the following programs, modulesand data structures, or a subset thereof:

-   -   an operating system 210, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module (or instructions) 212 for        connecting the device 102 with other devices (e.g., the        categorization system 106 and the devices 102B . . . 102N) via        one or more network interfaces 204 (wired or wireless), or the        communication network 104 (FIG. 1);    -   a message interaction event communication module 214 for        monitoring recipient initiated message interaction events and        communicating them, through network communication module 212, to        categorization system 106;    -   a messaging application 150 for processing and displaying        incoming and outgoing electronic messages, including messages        114-1-1 through 114-1-N of category 119-1, . . . , messages        114-Q-1 through 114-Q-M of category 119-Q, where 1, 2, . . . ,        Q, are the message categories in a set of message categories;    -   a customization module 110; and    -   an optional user interface module 226 for displaying user        interface components or controls (e.g., textbox, button, radio        button, drop-down list) to a user.

In some embodiments, the customization module 110 includes one or moreof the following: a starring module 216 to allow a user to star amessage for inclusion in a priority category; an organization module 218to allow a user to move a message from one category to another (e.g., bydragging dropping); a filtering module 220 for allowing a user tospecify a category rule for a message, and a labeling module 222allowing a user to customize clusters for messages (by removing systemcreated categories and/or creating additional categories.) Furthermore,the customization module 118 optionally includes one or more additionalcustomization modules 224 for providing further user customization ofcategorization rules.

In some implementations, the user interface 205 includes an input device(e.g., a keyboard, a mouse, a touchpad, a track pad, and a touch screen)for a user to interact with the device 102.

In some implementations, the labeling module 222 labels an electronicmessage using a flag in accordance with which category the electronicmessage has been assigned. For example, after an email is assigned toboth a “Travel” category and a “Promotion” category, the labeling module222 assigns both the label “Travel” and the label “Promotion” to theelectronic message. These approaches are advantageous, because messagelabels may simplify searches and selective retrievals of electronicmessages, e.g., electronic messages may be searched, and retrieved, bothusing labels.

In some implementations, one or more of the above identified elementsare stored in one or more of the previously mentioned memory devices,and correspond to a set of instructions for performing a functiondescribed above. The above identified modules or programs (e.g., sets ofinstructions) need not be implemented as separate software programs,procedures or modules, and thus various subsets of these modules may becombined or otherwise re-arranged in various implementations. In someimplementations, the memory 206 optionally stores a subset of themodules and data structures identified above. Furthermore, the memory206 may store additional modules and data structures not describedabove. In some embodiments, the device 102 is a thin client which doesnot include one or more of the customization modules 118 (e.g., thestarring module 216; organization module 218; filtering module 220;labeling module 222, etc), and as such categorization customization isperformed in part or in whole on the server categorization system 106.

FIG. 3A is a block diagram illustrating a categorization system 106, inaccordance with a more detailed implementation. The categorizationsystem 106 typically includes one or more processing units CPU(s) 302(also referred to as processors), one or more network interfaces 304,memory 306, and one or more communication buses 308 for interconnectingthese components. The communication buses 308 optionally includecircuitry (sometimes called a chipset) that interconnects and controlscommunications between system components. The memory 306 includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM or otherrandom access solid state memory devices; and optionally includesnon-volatile memory, such as one or more magnetic disk storage devices,optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. The memory 306 optionallyincludes one or more storage devices remotely located from CPU(s) 302.The memory 306, or alternatively the non-volatile memory device(s)within the memory 306, comprises a non-transitory computer readablestorage medium. In some implementations, the memory 306 or alternativelythe non-transitory computer readable storage medium stores the followingprograms, modules and data structures, or a subset thereof:

-   -   an operating system 310, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module (or instructions) 312 for        connecting the categorization system 106 with other devices        (e.g., the devices 102) via the one or more network interfaces        304 (wired or wireless), or the communication network 104 (FIG.        1);    -   a message classifier module 170 for conducting an analysis of a        first plurality of electronic messages thereby independently        identifying an initial message category in a set of message        categories for each respective message in the first plurality of        messages using a baseline message classifier 314;    -   an optional customization module 118 for allowing a user to        create and/or edit categorization rules in accordance with        various categorization actions;    -   a message delivery module 174 for delivering messages that have        been classified by message classifier module 170 to one or more        recipients;    -   a recipient initiated message interaction monitoring module 176        for collecting a plurality of recipient initiated message        interaction events for messages in the first plurality of        messages over a predetermined period of time from the plurality        of recipients and optionally storing such events in a recipient        initiated message interaction monitoring data store 182;    -   a message categorization dataset construction module 178 for        constructing a message categorization dataset (315) from (i) the        first plurality of messages, (ii) the designation of the message        category of each respective message in the plurality of messages        assigned by the message classifier module 170, and (iii) the        plurality of recipient initiated message interaction events        collected by recipient initiated message interaction monitoring        module 176;    -   a message queue 112 for storing electronic messages e.g., MSG 1,        MSG 2, MSG 3, . . . MSG N (114-1, . . . , 114-N) awaiting        processing by the message classifier module 170;    -   a classified message store 172, which includes a message        category 330 in the set of message categories for each        respective message analyzed by the message classifier module 170        as well as either the respective message or a link to the        respective message;    -   a classifier training/evaluation module 180 that uses the        message categorization dataset 315 to train or measure the        performance of a test message classifier 340.

In some embodiments, the customization module 118 includes one or moreof the following: a starring module 316 to allow a user to star amessage for inclusion in a priority category; an organization module 318to allow a user to move a message from one category to another (e.g., bydragging dropping), a filtering module 320 for allowing a user tospecify a category rule for a message, and a labeling module 322allowing a user to customize categories for message (by removing systemcreated categories and/or creating additional categories.) Furthermore,the customization module 118 optionally includes one or more additionalcustomization modules 324 for providing further user customization.

FIG. 3B illustrates an exemplary recipient initiated message interactionmonitoring data store 182 in accordance with some embodiments. Recipientinitiated message interaction monitoring module 176 receives events fromthe message interaction event monitoring/communication modules 214 ofrespective devices 102 in computing system 100 and stores them inrecipient initiated message interaction monitoring data store 182 in theform of data pairs, each data pair 194 comprising a message 114, or linkto the message, and a corresponding recipient initiated messageinteraction type 192. In some embodiments, a given message 114 isdelivered to multiple recipients. Accordingly, in some embodiments, itis possible for data store 182 to include interactions 192 from morethan recipient for a given message 114. In some embodiments, a givenmessage 114 includes multiple interactions 192 from a single recipientfor a given message 114. For example, in some embodiments, a singlerecipient opens a message 114, reviews the message, replies to themessage, and forwards the message. Each of these events constitutes arecipient initiated message interaction and accordingly, for someembodiments, such events are stored for a given message 114. It followsthat, in some embodiments, a given message 114 includes multipleinteractions 192 from each of a plurality of recipients for the givenmessage 114.

FIG. 3C illustrates an exemplary message categorization dataset 315 inaccordance with some embodiments. In some embodiments, messagecategorization dataset 315 includes at least a subset of the messages inthe original first plurality of messages (114-1, . . . , 114-N ofmessage queue 112). In some embodiments, message categorization dataset315 includes all the messages in the original first plurality ofmessages (114-1, . . . , 114-N of message queue 112). In the embodimentillustrated in FIG. 3C, for each such message 114 included in themessage categorization dataset 315, there is a copy or link (e.g.,pointer) to the message 114, a corresponding weight 196 for the message,and a message category 198 for the message. In some embodiments, themessage category 198 for a message is the message category initiallyassigned by message classifier module 170. However, in some embodiments,this is not necessarily the case. For instance, in some embodiments, themessage category 198 for a message 114 in message categorization dataset315 is one that has been assigned by the recipient of the message anddiffers from the initial category originally assigned to the message bythe message classifier module 170. In some embodiments, a weight 196 isassigned to one or more respective messages in the messagecategorization dataset that is determined as a function of the messageinteraction events associated with the respective messages in the datastore 182 (FIG. 3B). For instance, if there are one or more eventsassociated with a respective message 114 in data store 182 thatindicates explicit or tacit approval of the message category originallyassigned by the message classifier module 170, the weight 196 for therespective message is upweighted relative to those messages that lacksuch feedback or have incurred events that suggest recipientdisagreement with the initial category. In some embodiments, the weight196 assigned to a respective message is a function of not only therecipient interaction events associated with a respective message, butalso the original classifier score for the respective message determinedby the message classifier module 170 using the baseline messageclassifier 314. In such embodiments, recipient events that suggestrecipient agreement with the initial category (e.g., labeling themessage as important, replying to the message, forwarding the message,opening but not changing the category of the message), combined with aclassifier score from message classifier module 170 that indicates highconfidence in the initial message classification leads to an upweightedweight 196 for the respective message 114, whereas recipient events thatsuggest recipient disagreement with the initial category (e.g., categoryreassignment, deletion of message, etc.), combined with a classifierscore from message classifier module 170 that suggest low or marginalconfidence in the initial message classification leads to a downweightedweight 196 for the respective message 114.

In some implementations, one or more of the above identified elementsare stored in one or more of the previously mentioned memory devices,and correspond to a set of instructions for performing a functiondescribed above. The above identified modules or programs (e.g., sets ofinstructions) need not be implemented as separate software programs,procedures or modules, and thus various subsets of these modules may becombined or otherwise re-arranged in various implementations. In someimplementations, the memory 306 optionally stores a subset of themodules and data structures identified above. Furthermore, the memory306 may store additional modules and data structures not describedabove.

Although FIGS. 2 and 3 show a “device 102” and a “categorization system106,” respectively, FIGS. 2 and 3 are intended more as functionaldescription of the various features which may be present in computersystems than as a structural schematic of the implementations describedherein. In practice, and as recognized by those of ordinary skill in theart, items shown separately could be combined and some items could beseparated.

FIG. 4 is a flow chart illustrating a method for classifying messages ina computing system in accordance with some implementations. Inaccordance with the disclosed systems and methods, messages in a firstplurality of messages are classified, thereby independently identifyinga message category in a set of message categories for each respectivemessage in the first plurality of messages (402). In some embodiments,the plurality of messages includes messages from a plurality of senders.In some embodiments, the classifying is done by a baseline messageclassifier that was constructed at a time prior to the classifying 402.

In some embodiments, the messages in the first plurality of messages areclassified into the set of message categories using any of a number ofpossible techniques. In one example, the message classifier module 170uses the baseline message classifier 314 to examine the content of amessage. If the message contains words or phrases that are usuallyassociated with a particular category in the set of categories, themessage classifier classifies the message in that particular category.In another example, the baseline message classifier 314 compares thecontents of a message to be classified to previously classified emails.If the unclassified message is similar to one or more previously sentmessages, the message classifier classifies the message in the samecategory as the previously sent messages.

The categorized messages are delivered to recipients 102 (406).Recipients review the categorized messages and interact with them usingmessaging application 150 (406). Such interactions are termed recipientinitiated message interaction events. In some embodiments a recipientinitiated message interaction event is an event in which the recipientchanges the message category of a received message 114. FIG. 5illustrates. In FIG. 5, a portion of the interface of an exemplarymessaging application 150 running on a user device 102 is depicted. Theexemplary messaging application 150 receives the first plurality ofmessages and arranges the messages into the tabs 506 that correspond tomessage categories. For example, those messages in the first pluralityof messages that have been classified as “primary” by the baselinemessage classifier 314 are placed in message category tab 506-1, thosemessages in the first plurality of messages that have been classified as“social” by the test message classifier are placed in message categorytab 506-2, those messages in the first and plurality of messages thathave been classified as “promotions” are placed in message category tab506-3. In FIG. 5, tab 506-1 is featured, meaning that the messages 114in the category represented by tab 506-1 (primary) are listed in aspecified order. In the example illustrated in FIG. 5, this order ischronological, but other ordering is available.

FIG. 5 further illustrates how a recipient can initiate a messagecategory correction event. The user selects a listed message (e.g.,message 114-1), for example by right-clicking on the message with amouse, thereby bringing up a menu that includes correction event option502. By selecting correction event option 502, the user can change thecategory of the message to any of the other message category in a set ofmessage categories using selection panel 504 (available messagecategories). In some embodiments, the set of message categoriescomprises promotions, social, updates, forums, travel, finance andreceipts. In some embodiments, the set of message categories comprisesprimary, promotions, social, updates, forums, travel, finance andreceipts, or any subcombination thereof. In some embodiments, the set ofmessage categories is any combination primary, promotions, social,updates, and forums in addition to one or more user defined categories.

Returning to FIG. 4, in the disclosed methods, the interaction eventsthat are tracked are not limited to recipient initiated changes tomessage categories of received messages. In some embodiments, recipientinitiated events such as opening of messages 114, forwarding of messages114, replying to messages 114, reviewing messages 114, deleting messages114, labeling messages 114 as being important, are collected (e.g., overa predetermined period of time) from the recipients (408). In someembodiments, such collection is done on a recurring incremental basis,e.g., every hour, once a day, once a week. In some embodiments, suchcollection is done over a single predetermined period of time such asone hour, one day, one week, or one month. Data structure 182illustrates one such data structure of the information that is collectedin step 408. In some embodiments, data structure 182 includes additionalfields, such as an identifier for the recipients (e.g., in cases wherethere are multiple recipients of a given message), a time when the eventoccurred, etc. In particular, in some embodiments that track when eventsoccurred, more recent events can be given more significance whenassigning a weight to a given message or when deciding whether or not toinclude the message in the message categorization dataset 315.

In step 410, a message categorization dataset 315 is constructed from(i) the first plurality of messages, (ii) the designation of the messagecategory of each respective message in the subset of messages, and (iii)the plurality of recipient initiated message interaction events thatwere collected. As noted above with respect to step 408, in someembodiments events are collected on a rolling basis and, in suchembodiments, the messages categorization dataset is constructed on arolling basis.

In some embodiments step 410 involves identifying messages for messagecategorization dataset 315 that are not associated with recipientcorrective events such as manual recipient message recategorization. Inparticular embodiments, what is sought is a list of senders that areconsistently categorized into the same set of categories by the baselinemessage classifier in step 402, have a lot of deliveries (e.g., are wellrepresented by messages in the first plurality of messages) and very fewcorrective events. For such messages the initial designation by thebaseline classifier in step 402 can be more trusted. As such, in somesuch embodiments, what is sought is the identification of a plurality ofsenders whose messages in the first plurality of messages areconsistently categorized by the baseline message classifier into thesame respective message categories in the set of message categories and(ii) whose messages are associated with less than a predetermined amountof message interaction events. Precisely what constitutes being“consistently categorized” and “less than a predetermined amount” insuch embodiments is application dependent.

By way of example, in some embodiments, messages are deemed to be“consistently categorized” by the baseline message classifier into thesame respective message categories in the set of message categories instep 402 when at least a predetermined threshold percentage of themessages (e.g., at least seventy percent, at least eighty percent, atleast ninety percent, at least 98 percent, at least 99 percent) from theplurality of senders are categorized into two or less message categoriesin the set of message categories by the baseline message classifier.

By way of another example, in some embodiments, messages are deemed tobe “consistently categorized” by the baseline message classifier intothe same respective message categories in the set of message categoriesin step 402 when at least a predetermined threshold percentage of themessages (e.g., at least seventy percent, at least eighty percent, atleast ninety percent, at least 98 percent, at least 99 percent) from theplurality of senders are categorized into a single message category inthe set of message categories by the baseline message classifier. Inother words, the messages for each respective sender in the plurality ofsenders are classified into a single message category. In some suchembodiments, there is no requirement that each sender's messages becategorized into the same message category such that all messages fromthe plurality of senders are categorized into the same category. Rather,for example, all the messages from a first sender in the plurality ofsenders can be categorized to a first message category and all themessages from a second sender in the plurality of senders can becategorized to a second message category.

In such embodiments, the term “less than a predetermined amount” ofmessage interaction events is application dependent. In someembodiments, if any messages from a sender incur a manual recipientmessage category correction event, the sender and all the messages fromthe sender are disqualified from inclusion in the message categorizationdataset 315. In some embodiments, the messages from a respective senderqualify for inclusion in the message categorization dataset 315 whenless than a threshold percentage of the messages from the sender incur amanual recipient message category correction event (provided the aboveidentified “consistently categorizing” requirement is met). In someembodiments, such a threshold is not applied and, rather, a weight isassigned to a respective message in the message categorization dataset315 as a function of the consistency by which the respective messagesender's messages are historically categorized into particular messagecategories (e.g., the same message category, a select few messagecategories, etc.) and the frequency that messages from the sender of therespective message historically undergo recipient recategorizationevents (e.g., less than five percent of the time, less than 15 percentof the time). In some embodiments, such a threshold is not applied and,rather, a weight is assigned to a respective message as a function ofsome combination of (i) a score representative of the originalconfidence in the first classifier score for the respective message(from step 402), (ii) the consistency by which messages from the senderof the respective message are categorized into particular messagecategories (e.g., the same message category, a select few messagecategories, etc.), and (iii) the frequency that messages from the senderof the respective message historically undergo recipientrecategorization events (e.g., less than five percent of the time, lessthan 15 percent of the time).

In some embodiments, in order for a sender to qualify for evaluation asto whether the sender is one whose messages are consistently categorizedby the baseline message classifier into the same respective messagecategories in the set of message categories and (ii) whose messages areassociated with less than a predetermined amount of message interactionevents, the sender must have more than a threshold number of messages inthe first plurality of messages. This threshold number is applicationdependent and, for instance, will depend in some embodiments on thetotal number of messages there are in the first plurality of messages.In some embodiments, the threshold number is a number that is sufficientto have confidences that observations, such as message categorization(in step 402) and manual message recategorization (in step 406) havestatistical significance. In some such embodiments, this is achievedwhen the number of messages from a given sender exceeds ten messages. Insome such embodiments, this is achieved when the number of messages froma given sender exceeds one hundred messages.

In some alternative embodiments, the message categorization dataset 315is constructed by identifying recipient initiated message interactionevents in which the recipient changes the category of a message from themessage category assigned by the baseline message classifier to adifferent message category in the set of message categories. In suchembodiments, it is assumed that recipient selected message categories iscloser to the ground truth for such messages than message categoriesassigned by an automated classifier in step 402. An example of a messagethat would be included in the message categorization dataset 315 in suchembodiments is a message that was initially classified into the category“updates” in step 402 by the baseline message classifier but wasreclassified into the category “social” by the recipient of the messageusing messaging application 150 at an associated user device 102. Here,the new message category for the message, “social,” is consideredvalidated because the message recipient upon reviewing the message tookthe time and effort to reclassify the message. Embodiments of thepresent disclosure capitalize on such information to construct themessage categorization dataset 315. In some embodiments, such manualrecipient reclassification serves as just one factor in determiningwhich messages to include in the message categorization dataset. Forinstance, in some embodiments messages in the message categorizationdataset 315 are culled from the first plurality of messages, with eachsuch message in the dataset 315 being given a weight that is a functionof the combination of a confidence score assigned by the baselineclassifier in step 402 and a score given to messages when they undergo amanual recipient recategorization event. As another example, in someembodiments messages in the message categorization dataset 315 areculled from the first plurality of messages, with each such message inthe dataset 315 being given a weight that is a function of a first scoregiven to messages when they undergo a manual recipient recategorizationevent and a second score given to messages when they undergo positiverecipient initiated message interaction events such as being reviewedfor at least a predetermined amount of time, being forwarded, beingreplied to, or being labeled as special or important or a prioritymessage. Thus, in such embodiments, a message categorization dataset isbuilt using the identity of messages whose categories have beenreassigned by recipients, together with the message categoriesidentified for such recipients. In some embodiments, rather thanproviding a weight for each message in the message categorizationdataset, the selection of messages from the first plurality of messagesfor inclusion in the message categorization dataset 315 is strictlycategorical, with those messages in the first plurality of messages thathave been manually reassigned by recipients being included in thedataset 315 and those messages in the first plurality of messages thathave not been reassigned not being included in the dataset 315. In stillother embodiments, messages in the first plurality of messages thatreceived a score from the baseline classifier in step 402 that indicatesa high degree of confidence in the initial classification of suchmessages and messages that have been manually recategorized by messagerecipients, regardless of the classifier scores assigned to suchmessages in step 402, are included in the message categorization dataset315. For example, in some embodiments, a first message is included inthe dataset 315 because the baseline classifier assigned the firstmessage to a first message category with a high degree of confidence anda second message is included in the dataset 315 even thought thebaseline classifier in step 402 did not assign the second message amessage category with a high degree of confidence because the messagerecipient for the second message recategorized the message.

In still other embodiments, the message categorization dataset isconstructed from a subset of the first plurality of messages that areassociated with a positive recipient initiated message interactionevents. In some embodiments, a positive recipient initiated messageinteraction event is one in which a recipient of a respective message inthe first plurality of messages opens the respective message. In someembodiments, a positive recipient initiated message interaction event isone in which a recipient of a respective message in the first pluralityof messages opens the respective message for a predetermined amount oftime. Here, the predetermined amount of time is an amount of time thatis sufficient to have confidence that the user had an interest in themessage, and thus tacitly accepted the message categorization of themessage. Thus, the amount of time, in some embodiments, is one second ormore, 10 seconds or more. In some embodiments, rather than apredetermined amount of time, what is sought is an opening eventcombined with one or more scrolling events to ensure that the recipientreviewed the message and therefore tacitly agreed with the messagecategory. Accordingly, in some embodiments the respective message 114associated with the message opening event, and the category assigned tothe respective message by the baseline message classifier of step 402,is included in the message categorization dataset 315. In otherembodiments the respective message 114 associated with the messageopening event, and the category assigned to the respective message bythe baseline message classifier of step 402, is included in the messagecategorization dataset 315 with a weight whose value has beendetermined, at least in part, by the occurrence of the message openingevent associated with the respective message.

In some embodiments, a positive recipient initiated message interactionevent is one in which a recipient of a respective message in the firstplurality of messages assigns the respective message a prioritydesignation. For example, in the exemplary messaging application 150 ofFIG. 5, a user assigns a priority designation by using the star buttonnext to a respective message 114 to indicate that the respective messageis a priority message. Such a recipient initiated message interactionevent (starring the message) is deemed to be, in such embodiments, tacitagreement by the recipient that the baseline message classifier of step402 correctly categorized the message. Accordingly, in some embodimentsthe respective message 114, and the category assigned to the respectivemessage by the baseline message classifier of step 402, is included inthe message categorization dataset 315. In other embodiments therespective message 114, and the category assigned to the respectivemessage by the baseline message classifier of step 402, is included inthe message categorization dataset 315 with a weight whose value hasbeen determined, at least in part, by the occurrence of the prioritydesignation event associated with the respective message.

In some embodiments, a positive recipient initiated message interactionevent is one in which a recipient of a respective message in the firstplurality of messages replies to or forwards the respective message.Such a recipient initiated message interaction event is deemed to be, insuch embodiments, tacit agreement by the recipient that the baselinemessage classifier of step 402 correctly categorized the respectivemessage. Accordingly, in some embodiments the respective message 114,and the category assigned to the respective message by the baselinemessage classifier of step 402, is included in the messagecategorization dataset 315. In other embodiments the respective message114, and the category assigned to the respective message by the baselinemessage classifier of step 402, is included in the messagecategorization dataset 315 with a weight whose value has beendetermined, at least in part, by the occurrence of the message reply orforward event associated with the respective message.

The above identified embodiments of step 410 have identified certainpositive confirmatory events that help to validate the initial messagecategory assignments made to messages in the first plurality of messagesin step 402. In some embodiments, in addition to, or instead of suchpositive events, negative events are tracked in step 406, collected instep 408 and used in step 410 to help define and construct the messagecategorization dataset 315. For example, in some such embodiments, themessage categorization dataset comprises a subset of the plurality ofmessages, and the constructing comprises excluding from the subset ofthe first plurality of messages those messages in the first plurality ofmessages associated with a negative recipient initiated messageinteraction event. Examples of such negative recipient initiated messageinteraction event include, but are not limited to (i) a recipientchanging a message category of a respective message in the firstplurality of messages that was assigned to the respective message by thebaseline message classifier and (ii) a recipient deleting a receivedmessage without opening the message. In some embodiments, messageassociation with a negative recipient initiated message interactionevent is used to categorically exclude a respective message in the firstplurality of messages from inclusion in the message categorizationdataset. In some alternative embodiments, rather than altogetherexcluding a respective message in the first plurality of messages fromthe message categorization dataset 315, message association with anegative recipient initiated message interaction event is used todownweight the respective message in the first plurality of messages inthe message categorization dataset.

In step 412, the message categorization dataset 315 is used to train atest message classifier or measure the performance of the test messageclassifier. In some embodiments, the test message classifier and thebaseline message classifier are the same message classifier.

Examples of classifiers that are used as the test and/or baselinemessage classifier are described in, for example, Duda, 2001, PatternClassification, John Wiley & Sons, Inc., and Hastie et al., 2001, TheElements of Statistical Learning, Springer-Verlag, New York, each ofwhich are hereby incorporated by reference. For instance, examples ofclassifiers that are used in some embodiments for the test messageclassifier and/or the baseline message classifier include, but are notlimited to, decision trees, multiple additive regression trees, neuralnetworks, clustering, principal component analysis, nearest neighboranalysis, linear discriminant analysis, quadratic discriminant analysis,support vector machines, variants and derivatives thereof, all eitheralone or in any combination. Moreover, in some embodiments, the testmessage classifier and/or the baseline message classifier comprises aplurality of classifiers that have been combined using techniques suchas bagging, boosting, a random subspace method, additive trees, Adaboostor other known combining techniques. See, for example, Breiman, 1996,Machine Learning 24, 123-140, and Efron and Tibshirani, “An Introductionto Boostrap,” Chapman & Hall, New York, 1993, which is herebyincorporated by reference in its entirety.

As noted at the outset, in typical embodiments, message categories inthe set of message categories are problematic for a number of reasons,including the fact that the class distribution among the set of messageclasses is typically not balanced. That is, a great number of messagesfall into one category in the set of categories compared to othercategories in the set of categories. As such class distribution becomesmore skewed, evaluation of classifier performance based on accuracybecause less satisfactory. Furthermore, evaluation of a classifier byaccuracy also assumes equal error cost, that is the false positive error(e.g. recipient centric perceptual cost of a false positive) withrespect to a given message category is equivalent to the false negativeerror (e.g. recipient centric perceptual cost of a false negative) forthe respective message category. However, such an assumption often doesnot map to real world message categorization. that is, for some messagecategories, the error cost for false inclusion in the message categoryis not the same as the error cost for false exclusion from the messagecategory. Nevertheless, in some embodiments of the present disclosure,the message categories provided in the message categorization dataset315 are deemed to be the closest thing to ground truth that will berealized and a test classifier's accuracy in correctly guessing themessage category of each message in the message categorization dataset315 (against the message categories of such messages as set forth in thedataset) is used to measure the performance of the test classifier. Ifthe test message classifier is highly accurate, the test messageclassifier may be employed in the field to classify messages fordelivery to recipients.

In other embodiments, the message categorization dataset 315 is used tomeasure the performance of a test classifier using techniques other thanthose that are strictly based upon an accuracy metrics that are known inthe art. To consider classifier evaluation, let {p,n} be the positiveand negative instance classes, and let {Y, N} be the classificationsproduced by the test classifier for a given message class. Let p(p|I) bethe posterior probability that message I is positive for the givenmessage class. In some embodiments, the true positive rate, TP, of theclassifier for this message class is:

${TP} = {{p\left( {Y\text{|}p} \right)} \approx \frac{{positives}\mspace{14mu}{correctly}\mspace{14mu}{identified}}{{total}\mspace{14mu}{positives}}}$

In some embodiments, the false positive rate, FP, for the testclassifier for the given classifier is:

${FP} = {{p\left( {Y\text{|}n} \right)} \approx \frac{{negatives}\mspace{14mu}{incorrectly}\mspace{14mu}{identified}}{{total}\mspace{14mu}{positives}}}$

In some embodiments, if c(classification,class) is a two-place errorcost function where c(Y, n) is the cost of a false positive error andc(N, p) is the cost of a false negative error, I the decision to emit apositive classification is:[1−p(p|I)·c(Y,n)]<p(p|I)·c(N,p)

Regardless of whether the test classifier produces probabilistic orbinary classifications for a given message category, its normalized coston the message categorization dataset 315 can therefore be evaluatedempirically as:Cost=FP·c(Y,n)+FN·c(N,p)

Thus, in some embodiments, the test classifier is evaluated using themessage categorization dataset 315 by determining the cost using theabove-identified equation or some other type of cost functions. Such atechnique for evaluating classifiers is provided by way of illustrationonly and in fact, any method for using the message categorizationdataset 315 to evaluate the performance of the test message classifieris within the scope of the present disclosure. One such method is areceiver operator curve convex hull method disclosed in Provost andFawcett, 1997, “Analysis and Visualization of Classifier Performance:Comparison under Imprecise Class and Cost Distributions,” KDD-97Proceedings pp 43-48, which is hereby incorporated by reference, as anonlimiting example of alternatives to classifier accuracy measurements.

In some embodiments, rather than evaluating the performance of the testmessage classifier, the message categorization dataset 315 is used totrain the message classifier. Ways in which the message categorizationdataset 315 is used to train the test message classifier depend upon thenature of the classifier (e.g., whether the classifier is a neuralnetwork, support vector machine, decision tree, etc.). Examples oftraining classifiers are given, for example, in Duda et al., 2001,Pattern Classification, Second Edition, John Wiley & Sons, Inc., NewYork which is hereby incorporated by reference for the purpose ofillustrating nonlimiting examples of ways to train classifiers using adataset such as the message categorization dataset 315.

In this description, reference has been made to a test messageclassifier. It will be appreciated that the systems and methods of thepresent disclosure can be use to either train or measure the performanceof any number of classifiers. Reference is given to the “testclassifier” to identify any one such classifier.

FIG. 6 illustrates a flow chart for methods 600 of classifying messagesat a computer system having one or more processors, and memory storingone or more programs for execution by the one or more processors (602).Each message in a first plurality of messages is classified, therebyindependently identifying a message category in a set of messagecategories for each respective message in the first plurality ofmessages (604). In some embodiments, the classifying (604) is performedby a baseline message classifier that was constructed (e.g., trained) ata time prior to the classifying (606). In some embodiments, the baselinemessage classifier is trained at a time prior to the classifying (604)using data other than the first plurality of messages (606). In someembodiments, the set of message categories comprises promotions, social,updates, forums, travel, finance and receipts (608). In someembodiments, each message in the first plurality of messages comprisesan Email, short message service (SMS) text message, a multimediamessaging service (MMS) message, a file, a document, a video, an image,or an electronic conversation (610).

The method continues with the delivery of the first plurality ofmessages to a plurality of recipients with a designation of the messagecategory of each respective message in the first plurality of messages(612). In some embodiments, as illustrated in FIG. 1, each recipient inthe plurality of recipients is associated with a client 102 in aplurality of clients and the delivering comprises delivering eachrespective message in the first plurality of messages to the respectiveclient 102 in the plurality of clients that is associated with therecipient(s) of the respective message (614).

In some embodiments, a plurality of recipient initiated messageinteraction events is collected for messages in the first plurality ofmessages over a predetermined period of time from the plurality ofrecipients (616). Then, a message categorization dataset 315 isconstructed from (i) the first plurality of messages, (ii) thedesignation of the message category of each respective message in theplurality of messages, and (iii) the plurality of recipient initiatedmessage interaction events (618).

In some embodiments in accordance with step (618), the classifying ofstep 604 is performed by a baseline message classifier constructed at atime prior to the classifying, the plurality of recipient initiatedmessage interaction events collected in step 616 are message categorycorrection events in which a recipient has changed the category of amessage from the message category assigned by the baseline messageclassifier to a different message category in the set of messagecategories, and the constructing 618 comprises identifying a pluralityof senders (i) whose messages in the first plurality of messages areconsistently categorized by the baseline message classifier into thesame respective message categories in the set of message categories and(ii) whose messages are associated with less than a predetermined amountof message interaction events (620). In some such embodiments, messagesin the first plurality of messages are consistently categorized by thebaseline message classifier into the same respective message categoriesin the set of message categories when at least a predetermined thresholdpercentage of the messages from the plurality of senders are categorizedinto two or less message categories in the set of message categories bythe baseline message classifier (622). In some such embodiments, eachsender in the plurality of senders has more than a threshold number ofmessages in the first plurality of messages (624). In some suchembodiments, the message categorization dataset 315 comprises a subsetof the first plurality of messages (626).

In other embodiments, the message categorization dataset 315 comprises asubset of the first plurality of messages, the classifying 604 isperformed by a baseline message classifier that was constructed at atime prior to the classifying, the plurality of recipient initiatedmessage interaction events are events in which the recipient changes thecategory of a message from the message category assigned by the baselinemessage classifier to a different message category in the set of messagecategories, and the constructing 618 comprises selecting, for the subsetof the first plurality of messages, those messages in the firstplurality of messages that have undergone a recipient initiated messageinteraction event (628).

In still other embodiments, the message categorization dataset 315comprises a subset of the first plurality of messages, and theconstructing (618) comprises selecting, for the subset of the firstplurality of messages, those messages in the first plurality of messagesassociated with a (positive) recipient initiated message interactionevent (630), such as opening the respective message for at least apredetermined amount of time (632), assigning the respective message apriority designation (634), replying to the respective message (636), orforwarding the respective message (638).

In some embodiments, the message categorization dataset 315 comprises asubset of the first plurality of messages, and the constructing (618)comprises excluding from the subset of the first plurality of messagesthose messages in the first plurality of messages associated with a(negative) recipient initiated message interaction event (640), such asa recipient changing a message category of a respective message in thefirst plurality of messages that was assigned to the respective messageby the baseline message classifier (642) or deleting a respectivemessage in the first plurality of messages (644).

In some embodiments, the constructing a message categorization dataset(618) comprises independently assigning a weight to a respective messagein the plurality of messages based upon one or more recipient initiatedmessage interaction events that are associated with the respectivemessage (646). In some embodiments, such a weight is a categoricalweight, and the assigning assigns a first value to the weight when theone or more recipient initiated message interaction events are positiveinteractions, and the assigning assigns a second value to the weightwhen the one or more recipient initiated message interaction events arenegative interactions (648). In other embodiments, the weight is acontinuous weight, and the assigning assigns a value to the weight as afunction of the one or more recipient initiated message interactionevents associated with the respective message (650).

The method continues with the message categorization dataset 315 beingused to train a test message classifier or measure the performance ofthe test message classifier (652). Optionally, in some embodiments, eachmessage in a second plurality of messages is classified using the testmessage classifier, thereby independently identifying a message categoryin the set of message categories for each respective message in thesecond plurality of messages (654). Optionally, in some embodiments, thesecond plurality of messages is delivered to the plurality of recipientswith a designation of the message category of each respective message inthe second plurality of messages, as respectively determined by the testmessage classifier (656).

Plural instances may be provided for components, operations orstructures described herein as a single instance. Finally, boundariesbetween various components, operations, and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within the scope of the implementation(s).In general, structures and functionality presented as separatecomponents in the example configurations may be implemented as acombined structure or component. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents. These and other variations, modifications, additions, andimprovements fall within the scope of the implementation(s).

It will also be understood that, although the terms “first,” “second,”etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first object couldbe termed a second object, and, similarly, a second object could betermed a first object, without changing the meaning of the description,so long as all occurrences of the “first object” are renamedconsistently and all occurrences of the “second object” are renamedconsistently. The first object and the second object are both objects,but they are not necessarily the same object unless otherwise indicatedherein.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of the claims.As used in the description of the implementations and the appendedclaims, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.'

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined (that a stated condition precedent is true)” or “if (a statedcondition precedent is true)” or “when (a stated condition precedent istrue)” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

The foregoing description included example systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative implementations. For purposes of explanation,numerous specific details were set forth in order to provide anunderstanding of various implementations of the inventive subjectmatter. It will be evident, however, to those skilled in the art thatimplementations of the inventive subject matter may be practiced withoutthese specific details. In general, well-known instruction instances,protocols, structures and techniques have not been shown in detail.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit the implementations to the precise forms disclosed. Manymodifications and variations are possible in view of the aboveteachings. The implementations were chosen and described in order tobest explain the principles and their practical applications, to therebyenable others skilled in the art to best utilize the implementations andvarious implementations with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer-based method of classifying messages,the method comprising: collecting, by one or more processors, aplurality of recipient initiated message interaction events for messagesin a first plurality of messages from a plurality of recipients,including a plurality of priority designation assignments and aplurality of message category change events in which a recipient haschanged a category of a given message in the first plurality of messagesfrom an original message category to an updated message category;identifying, by the one or more processors, a subset of the firstplurality of messages as having trusted message category assignmentsbased on the plurality of recipient initiated message interactionevents; constructing, by the one or more processors, a messagecategorization dataset from the identified subset of the first pluralityof messages, the constructing including selecting messages in the firstplurality of messages that have undergone a recipient initiated messageinteraction event, wherein the message categorization dataset comprises,for each selected message from the identified subset, informationidentifying the message and information corresponding to a currentlyassigned message category for the message; training, by the one or moreprocessors, a message classifier using the message categorizationdataset; after training the message classifier, classifying each messagein a second plurality of messages using the message classifier by theone or more processors; and delivering the second plurality of messagesto one or more recipients with a designation of a respective messagecategory of each message in the second plurality of messages, asdetermined by the message classifier.
 2. The computer-based method ofclaim 1, wherein the message categorization dataset further comprises,for each selected message from the identified subset, informationcorresponding to a level of confidence in the original message categoryfor the selected message.
 3. The computer-based method of claim 2,wherein the information corresponding to the level of confidence in theoriginal message category for each selected message comprises a weightassigned to the respective selected message.
 4. The computer-basedmethod of claim 1, wherein the constructing the message categorizationdataset further includes assigning a weight to each respective messagein the plurality of messages based upon one or more recipient initiatedmessage interaction events that are associated with the respectivemessage.
 5. The computer-based method of claim 4, wherein: the assigningassigns a first value to the weight for a respective message when theone or more recipient initiated message interaction events for therespective message are positive interactions with respect to theoriginal message category of the respective message; and the assigningassigns a second value to the weight for the respective message when theone or more recipient initiated message interaction events for therespective message are negative interactions with respect to theoriginal message category of the respective message.
 6. Thecomputer-based method of claim 5, wherein the positive interactionsindicate explicit or tacit approval of the original message category. 7.The computer-based method of claim 5, wherein the negative interactionsindicate disagreement with the original message category.
 8. Thecomputer-based method of claim 7, wherein the negative interactionsinclude either a category reassignment or deletion of a message.
 9. Thecomputer-based method of claim 4, wherein the weight is a weight on acontinuous scale, and wherein the assigning assigns a value to theweight as a function of the one or more recipient initiated messageinteraction events associated with the respective message.
 10. Thecomputer-based method of claim 4, wherein assigning the weight isfurther based on an original classifier score for each respectivemessage determined using a baseline message classifier different fromthe trained message classifier.
 11. The computer-based method of claim1, further comprising, prior to the collecting, identifying a pluralityof senders whose messages satisfy a predetermined number of messagecategory change events.
 12. The computer-based method of claim 1,further comprising, prior to the collecting, identifying a plurality ofsenders whose have a threshold number of messages in the first pluralityof messages.
 13. The computer-based method of claim 1, wherein theselecting the messages in the first plurality of messages includesselecting a given message where a recipient of the given message repliedto the given message, or selecting a given message where the recipientof the given message forwarded the respective message.
 14. Thecomputer-based method of claim 1, wherein each message in the firstplurality of messages comprises a calendar entry, an email, shortmessage service (SMS) text message, a multimedia messaging service (MMS)message, a file, a document, a video, an image, or an electronicconversation.
 15. A computing system, comprising: one or moreprocessors; memory storing one or more programs to be executed by theone or more processors, wherein the one or more programs compriseinstructions for: collecting a plurality of recipient initiated messageinteraction events for messages in a first plurality of messages from aplurality of recipients, including a plurality of priority designationassignments and a plurality of message category change events in which arecipient has changed a category of a given message in the firstplurality of messages from an original message category to an updatedmessage category; identifying a subset of the first plurality ofmessages as having trusted message category assignments based on theplurality of recipient initiated message interaction events;constructing a message categorization dataset from the identified subsetof the first plurality of messages, the constructing including selectingmessages in the first plurality of messages that have undergone arecipient initiated message interaction event, wherein the messagecategorization dataset comprises, for each selected message from theidentified subset, information identifying the message and informationcorresponding to a currently assigned message category for the message;training a message classifier using the message categorization dataset;after training the message classifier, classifying each message in asecond plurality of messages using the message classifier by the one ormore processors; and delivering the second plurality of messages to oneor more recipients with a designation of a respective message categoryof each message in the second plurality of messages, as determined bythe message classifier.
 16. The computing system of claim 15, whereinthe constructing the message categorization dataset further includesassigning a weight to each respective message in the plurality ofmessages based upon one or more recipient initiated message interactionevents that are associated with the respective message.
 17. The computersystem of claim 16, wherein: the assigning assigns a first value to theweight for a respective message when the one or more recipient initiatedmessage interaction events for the respective message are positiveinteractions with respect to the original message category of therespective message; and the assigning assigns a second value to theweight for the respective message when the one or more recipientinitiated message interaction events for the respective message arenegative interactions with respect to the original message category ofthe respective message.
 18. The computer system of claim 16, wherein theweight is a weight on a continuous scale, and wherein the assigningassigns a value to the weight as a function of the one or more recipientinitiated message interaction events associated with the respectivemessage.
 19. The computer system of claim 16, wherein assigning theweight is further based on an original classifier score for eachrespective message determined using a baseline message classifierdifferent from the trained message classifier.
 20. A non-transitorycomputer readable storage medium storing instructions for execution byone or more processors, the instructions, when executed, causing the oneor more processors to perform a method of: collecting a plurality ofrecipient initiated message interaction events for messages in a firstplurality of messages from a plurality of recipients, including aplurality of priority designation assignments and a plurality of messagecategory change events in which a recipient has changed a category of agiven message in the first plurality of messages from an originalmessage category to an updated message category; identifying a subset ofthe first plurality of messages as having trusted message categoryassignments based on the plurality of recipient initiated messageinteraction events; constructing a message categorization dataset fromthe identified subset of the first plurality of messages, theconstructing including selecting messages in the first plurality ofmessages that have undergone a recipient initiated message interactionevent, wherein the message categorization dataset comprises, for eachselected message from the identified subset, information identifying themessage and information corresponding to a currently assigned messagecategory for the message; training a message classifier using themessage categorization dataset; after training the message classifier,classifying each message in a second plurality of messages using themessage classifier by the one or more processors; and delivering thesecond plurality of messages to one or more recipients with adesignation of a respective message category of each message in thesecond plurality of messages, as determined by the message classifier.